Lisbon Treaty
The Dataset contains 338 columns and 1002 Rows, where rows contain the data of the individual choices and their age,education,sex etc.As my task is restricted on some factors only so i will not use the whole dataset.I will create a subset of my original dataset where i keep those rows only which is needed for calculation.
Lisbon_data2 <- Lisbon_data[,c(1:3,5,6,9,11,128:137,338)]
head(Lisbon_data2)## qb q1a q1b q4 q5 q8 q9b sex marital age class region area work indust
## 1 2 3 NA 3 -99.99 10 2 3 2 3 4 3 3 4 -99.99
## 2 2 2 3 2 3.00 7 2 2 2 5 6 3 3 3 5.00
## 3 2 3 NA 2 3.00 7 3 2 3 3 6 3 3 5 -99.99
## 4 2 2 2 2 3.00 7 3 2 2 5 2 3 3 3 13.00
## 5 2 2 3 2 3.00 7 3 2 2 5 4 3 3 4 -99.99
## 6 2 3 NA 2 3.00 8 4 2 3 4 5 3 3 6 -99.99
## sector tea exage
## 1 3.00 3 23
## 2 3.00 3 47
## 3 -99.99 3 22
## 4 3.00 3 38
## 5 3.00 3 44
## 6 -99.99 3 25Next i rename the columns of the dataset to better understanding of what they represent so that i can interpret my solution based on that
colnames(Lisbon_data2) <- c("Registered_Voter","oct_vote","oct_type","June_vote",
"June_type","EU_Knowledge","Switzerland_Member","Sex",
"Marital","Age_type","Class","Region","Area_type",
"Working_status","Industry_type",
"Working_sector","Education_status","Age")
Lisbon_data2[1:10,1:5]## Registered_Voter oct_vote oct_type June_vote June_type
## 1 2 3 NA 3 -99.99
## 2 2 2 3 2 3.00
## 3 2 3 NA 2 3.00
## 4 2 2 2 2 3.00
## 5 2 2 3 2 3.00
## 6 2 3 NA 2 3.00
## 7 2 2 2 2 2.00
## 8 2 2 2 2 3.00
## 9 2 2 2 2 2.00
## 10 2 2 2 2 2.00#Next i group the eu knowledge parameter into 3 section, poor good and average.
Lisbon_data2$EU_Knowledge[Lisbon_data2$EU_Knowledge >7] <- "Good"
Lisbon_data2$EU_Knowledge[Lisbon_data2$EU_Knowledge <= 7 &
Lisbon_data2$EU_Knowledge >= 4] <- "Average"
Lisbon_data2$EU_Knowledge[Lisbon_data2$EU_Knowledge < 4] <- "Poor"#except the age attribute all the variable present in my dataset is categorical so i need to transform it into factors.
Lisbon_data2$Registered_Voter <- as.factor(Lisbon_data2$Registered_Voter)
Lisbon_data2$oct_vote <- as.factor(Lisbon_data2$oct_vote)
Lisbon_data2$oct_type <- as.factor(Lisbon_data2$oct_type)
Lisbon_data2$June_vote <- as.factor(Lisbon_data2$June_vote)
Lisbon_data2$June_type <- as.factor(Lisbon_data2$June_type)
Lisbon_data2$EU_Knowledge <- as.factor(Lisbon_data2$EU_Knowledge)
Lisbon_data2$Switzerland_Member <- as.factor(Lisbon_data2$Switzerland_Member)
Lisbon_data2$Sex <- as.factor(Lisbon_data2$Sex)
Lisbon_data2$Marital <- as.factor(Lisbon_data2$Marital)
Lisbon_data2$Age_type <- as.factor(Lisbon_data2$Age_type)
Lisbon_data2$Class <- as.factor(Lisbon_data2$Class)
Lisbon_data2$Region <- as.factor(Lisbon_data2$Region)
Lisbon_data2$Area_type <- as.factor(Lisbon_data2$Area_type)
Lisbon_data2$Working_status <- as.factor(Lisbon_data2$Working_status)
Lisbon_data2$Industry_type <- as.factor(Lisbon_data2$Industry_type)
Lisbon_data2$Working_sector <- as.factor(Lisbon_data2$Working_sector)
Lisbon_data2$Education_status <- as.factor(Lisbon_data2$Education_status)
#In my dataset the categories coded with number, it's good for efficency of the model but now my task is to explain and understand the behaviour of each category for that i have to relabel it into their original names.
levels(Lisbon_data2$Registered_Voter) <- c("Yes","No","Unknown")
levels(Lisbon_data2$oct_vote) <- c("Vote","Didn't Vote","Unknown")
levels(Lisbon_data2$oct_type) <- c("Favour","Against","Didn't Vote")
levels(Lisbon_data2$June_vote) <- c("Vote","Didn't Vote","Unknown")
levels(Lisbon_data2$June_type) <- c("Favour","Against","Didn't Vote")
levels(Lisbon_data2$Switzerland_Member) <- c("True","False","Don't Know")
levels(Lisbon_data2$Sex) <- c("Male","Female")
levels(Lisbon_data2$Marital) <- c("Married","Single","Other")
levels(Lisbon_data2$Age_type) <- c("18-21","22-24","25-34","35-49",
"50-64","65+")
levels(Lisbon_data2$Class) <- c("Administrative","Supervisory","Skilled Manual",
"Unskilled manual","Farmers(>50 acre land)",
"Farmers(<50 acre land)")
levels(Lisbon_data2$Region) <- c("Dublin","Leinster","Munster","Ulster")
levels(Lisbon_data2$Area_type) <- c("Urban","Rural")
levels(Lisbon_data2$Working_status) <- c("Housewife","Self_Employed","Employee",
"Unemployed(Job Searching)",
"Unemployed(Not searching jobs)",
"Student","Retired")
levels(Lisbon_data2$Industry_type) <- c("Unknown","Construction","Computer/IT",
"Finance","Agriculture","Food Production",
"Government","Leisure","Manufacturing",
"Media","Profesionals","Retail",
"Other Services","Other")
levels(Lisbon_data2$Working_sector) <- c("Unknown","Public","Private")
levels(Lisbon_data2$Education_status) <- c("Primary","Secondary","Third level",
"School/Colllege")
Lisbon_data2$oct_type[is.na(Lisbon_data2$oct_type)] <- "Didn't Vote"
summary(Lisbon_data2)## Registered_Voter oct_vote oct_type June_vote
## Yes :964 Vote :756 Favour :485 Vote :724
## No : 19 Didn't Vote:241 Against :271 Didn't Vote:261
## Unknown: 19 Unknown : 5 Didn't Vote:246 Unknown : 17
##
##
##
##
## June_type EU_Knowledge Switzerland_Member Sex Marital
## Favour :278 Average:572 True :243 Male :492 Married:591
## Against :311 Good :385 False :524 Female:510 Single :318
## Didn't Vote:413 Poor : 45 Don't Know:235 Other : 93
##
##
##
##
## Age_type Class Region Area_type
## 18-21: 63 Administrative :134 Dublin :290 Urban:624
## 22-24: 70 Supervisory :311 Leinster:248 Rural:378
## 25-34:213 Skilled Manual :236 Munster :288
## 35-49:299 Unskilled manual :260 Ulster :176
## 50-64:225 Farmers(>50 acre land): 43
## 65+ :132 Farmers(<50 acre land): 18
##
## Working_status Industry_type Working_sector
## Housewife :188 Unknown :537 Unknown:447
## Self_Employed :102 Other Services:121 Public :151
## Employee :453 Government :103 Private:404
## Unemployed(Job Searching) : 66 Construction : 51
## Unemployed(Not searching jobs): 30 Agriculture : 39
## Student : 59 Finance : 35
## Retired :104 (Other) :116
## Education_status Age
## Primary : 75 Min. :18.00
## Secondary :588 1st Qu.:30.00
## Third level :280 Median :42.00
## School/Colllege: 59 Mean :43.59
## 3rd Qu.:56.00
## Max. :82.00
## Q.2
library(ggplot2)
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutattach(Lisbon_data2)
#Bivariate Plot's
ggplot(Lisbon_data2,mapping = aes(oct_vote,fill = oct_type)) + geom_bar() +
geom_text(aes(label=..count..),stat="count",position=position_stack(0.5)) +
ggtitle("Distribution of Votes based on the parameters that and individual voted or not")
#To create a pie chart of the above graph i have to build a table and then plot it.
oct <- as.data.frame(prop.table(table(oct_type)))
plot_ly(oct, labels = ~oct_type, values = ~Freq,
type = 'pie',textposition = 'outside',textinfo = 'label+percent') %>%
layout(title = "Voting percentage by considering those who didn't vote")#Now let's build a pie chart by considering those individual's only who voted in the referendum.
oct2 <- Lisbon_data2$oct_type[Lisbon_data2$oct_type != "Didn't Vote"]
oct3 <- as.data.frame(table(oct2))
oct3 <- oct3[1:2,]
oct3## oct2 Freq
## 1 Favour 485
## 2 Against 271plot_ly(oct3, labels = ~oct2, values = ~Freq,
type = 'pie',textposition = 'outside',textinfo = 'label+percent') %>%
layout(title = "Voting percentage after removing those who didn't vote")Q.3
ggplot(Lisbon_data2,mapping = aes(oct_type,Age,fill = oct_type))+geom_boxplot()+
geom_boxplot(width=0.8) + stat_summary(geom="text", fun.y=quantile,
aes(label=sprintf("%1.1f", ..y..), color=oct_type),
position=position_nudge(x=0.5), size=3.5) +
ggtitle("Distribution of Age according to the Vote's Given")## Warning: `fun.y` is deprecated. Use `fun` instead.
table(oct_type,Age_type)## Age_type
## oct_type 18-21 22-24 25-34 35-49 50-64 65+
## Favour 19 23 93 147 127 76
## Against 12 13 56 94 65 31
## Didn't Vote 32 34 64 58 33 25ggplot(Lisbon_data2,mapping = aes(oct_vote,fill = Age_type))+geom_bar()+
facet_grid(~Age_type) +
geom_text(aes(label=..count..),stat="count",position=position_stack(0.5))+
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("Distribution of individual who voted or not based on age group")
ggplot(Lisbon_data2,mapping = aes(oct_type,fill = Age_type)) + geom_bar()+
facet_grid(~Age_type) +
geom_text(aes(label=..count..),
stat="count",position=position_stack(0.5)) +
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("distribution of voting output based on the age group")
ggplot(Lisbon_data2,mapping = aes(oct_type,fill = Age_type)) + geom_bar()+
facet_grid(~Age_type) +
geom_text(aes(label=round((..count../sum(..count..))*100,2)),
stat="count",position=position_stack(0.5)) +
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("Percentage distribution of voting output based on the age group")
Lisbon_data3 <- Lisbon_data2[Lisbon_data2$oct_type != "Didn't Vote",]
head(Lisbon_data3)## Registered_Voter oct_vote oct_type June_vote June_type EU_Knowledge
## 2 Yes Vote Against Vote Didn't Vote Average
## 4 Yes Vote Favour Vote Didn't Vote Average
## 5 Yes Vote Against Vote Didn't Vote Average
## 7 Yes Vote Favour Vote Against Good
## 8 Yes Vote Favour Vote Didn't Vote Average
## 9 Yes Vote Favour Vote Against Average
## Switzerland_Member Sex Marital Age_type Class Region
## 2 True Male Married 35-49 Farmers(>50 acre land) Leinster
## 4 False Male Married 35-49 Administrative Leinster
## 5 False Male Married 35-49 Skilled Manual Leinster
## 7 Don't Know Female Married 65+ Farmers(>50 acre land) Leinster
## 8 False Female Married 50-64 Unskilled manual Munster
## 9 False Male Other 65+ Unskilled manual Munster
## Area_type Working_status Industry_type Working_sector Education_status Age
## 2 Rural Self_Employed Agriculture Private Secondary 47
## 4 Rural Self_Employed Other Services Private Secondary 38
## 5 Rural Employee Unknown Private Secondary 44
## 7 Rural Housewife Unknown Unknown Primary 76
## 8 Urban Housewife Unknown Unknown Third level 52
## 9 Urban Retired Unknown Unknown Primary 69#next consider those individual only who voted in the referendum.
ggplot(Lisbon_data3,mapping = aes(oct_type,Age,fill = oct_type))+geom_boxplot()+
geom_boxplot(width=0.8) + stat_summary(geom="text", fun.y=quantile,
aes(label=sprintf("%1.1f", ..y..), color=oct_type),
position=position_nudge(x=0.5), size=3.5) +
ggtitle("Distribution of age of the people who voted in the referendum")## Warning: `fun.y` is deprecated. Use `fun` instead.
ggplot(Lisbon_data3,mapping = aes(oct_type,fill = Age_type)) + geom_bar()+
facet_grid(~Age_type) +
geom_text(aes(label=..count..),stat="count",position=position_stack(0.5)) +
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("distribution of voting output based on the age group")
ggplot(Lisbon_data3,mapping = aes(oct_type,fill = Age_type)) + geom_bar()+
facet_grid(~Age_type) + geom_text(aes(label=round((..count../sum(..count..))*100,2)),
stat="count",position=position_stack(0.5)) +
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("Percentage distribution of voting output based on the age group")
#Next i will create a table for the same.
table(Lisbon_data3$oct_type,Lisbon_data3$Age_type)##
## 18-21 22-24 25-34 35-49 50-64 65+
## Favour 19 23 93 147 127 76
## Against 12 13 56 94 65 31
## Didn't Vote 0 0 0 0 0 0prop.table(table(Lisbon_data3$oct_type,Lisbon_data3$Age_type),1)##
## 18-21 22-24 25-34 35-49 50-64 65+
## Favour 0.03917526 0.04742268 0.19175258 0.30309278 0.26185567 0.15670103
## Against 0.04428044 0.04797048 0.20664207 0.34686347 0.23985240 0.11439114
## Didn't Voteprop.table(table(Lisbon_data3$oct_type,Lisbon_data3$Age_type),2)##
## 18-21 22-24 25-34 35-49 50-64 65+
## Favour 0.6129032 0.6388889 0.6241611 0.6099585 0.6614583 0.7102804
## Against 0.3870968 0.3611111 0.3758389 0.3900415 0.3385417 0.2897196
## Didn't Vote 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000Q.4
ggplot(Lisbon_data2,mapping = aes(oct_vote,fill = Class)) + geom_bar() +
facet_grid(~Class) + geom_text(aes(label=..count..),
stat="count",position=position_stack(0.5)) +
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("Distribution of individual who voted or not based on class")
ggplot(Lisbon_data2,mapping = aes(oct_type , fill = Class)) + geom_bar() +
facet_grid(~Class) + geom_text(aes(label=..count..)
,stat="count",position=position_stack(0.5))+
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("distribution of voting output based on the economic class")
ggplot(Lisbon_data2,mapping = aes(oct_type , fill = Class)) + geom_bar() +
facet_grid(~Class) + geom_text(aes(label=round((..count../sum(..count..))*100,2)),
stat="count",position=position_stack(0.5)) +
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("Percentage distribution of voting output based on the economic class")
table(oct_type,Class)## Class
## oct_type Administrative Supervisory Skilled Manual Unskilled manual
## Favour 86 169 89 103
## Against 22 79 76 81
## Didn't Vote 26 63 71 76
## Class
## oct_type Farmers(>50 acre land) Farmers(<50 acre land)
## Favour 30 8
## Against 7 6
## Didn't Vote 6 4prop.table(table(oct_type,Class))## Class
## oct_type Administrative Supervisory Skilled Manual Unskilled manual
## Favour 0.085828343 0.168662675 0.088822355 0.102794411
## Against 0.021956088 0.078842315 0.075848303 0.080838323
## Didn't Vote 0.025948104 0.062874251 0.070858283 0.075848303
## Class
## oct_type Farmers(>50 acre land) Farmers(<50 acre land)
## Favour 0.029940120 0.007984032
## Against 0.006986028 0.005988024
## Didn't Vote 0.005988024 0.003992016#consider those only who voted in the referendum.
ggplot(Lisbon_data3,mapping = aes(oct_type , fill = Class)) + geom_bar() +
facet_grid(~Class) + geom_text(aes(label=..count..)
,stat="count",position=position_stack(0.5))+
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("distribution of voting output based on the economic class")
ggplot(Lisbon_data3,mapping = aes(oct_type , fill = Class)) + geom_bar() +
facet_grid(~Class) + geom_text(aes(label=round((..count../sum(..count..))*100,2)),
stat="count",position=position_stack(0.5))+
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("distribution of voting output based on the economic class")
table(Lisbon_data3$oct_type,Lisbon_data3$Class)##
## Administrative Supervisory Skilled Manual Unskilled manual
## Favour 86 169 89 103
## Against 22 79 76 81
## Didn't Vote 0 0 0 0
##
## Farmers(>50 acre land) Farmers(<50 acre land)
## Favour 30 8
## Against 7 6
## Didn't Vote 0 0prop.table(table(Lisbon_data3$oct_type,Lisbon_data3$Class))##
## Administrative Supervisory Skilled Manual Unskilled manual
## Favour 0.113756614 0.223544974 0.117724868 0.136243386
## Against 0.029100529 0.104497354 0.100529101 0.107142857
## Didn't Vote 0.000000000 0.000000000 0.000000000 0.000000000
##
## Farmers(>50 acre land) Farmers(<50 acre land)
## Favour 0.039682540 0.010582011
## Against 0.009259259 0.007936508
## Didn't Vote 0.000000000 0.000000000Q.5
ggplot(Lisbon_data2,mapping = aes(oct_vote,fill = Education_status))+geom_bar()+
geom_text(aes(label=..count..),stat="count",position=position_stack(0.5)) +
facet_grid(~Education_status)+
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("Distribution of individual who voted or not based on Education status")
ggplot(Lisbon_data2,mapping = aes(oct_type,fill = Education_status))+geom_bar()+
geom_text(aes(label=..count..),stat="count",position=position_stack(0.5)) +
facet_grid(~Education_status)+
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("distribution of voting output based on Education status")
ggplot(Lisbon_data2,mapping = aes(oct_type,fill = Education_status))+geom_bar()+
geom_text(aes(label=round((..count../sum(..count..))*100,2)),
stat="count",position=position_stack(0.5)) + facet_grid(~Education_status)+
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("Percentage distribution of voting output based on Education status")
attach(Lisbon_data3)## The following objects are masked from Lisbon_data2:
##
## Age, Age_type, Area_type, Class, Education_status, EU_Knowledge,
## Industry_type, June_type, June_vote, Marital, oct_type, oct_vote,
## Region, Registered_Voter, Sex, Switzerland_Member, Working_sector,
## Working_statustable(oct_type,Education_status)## Education_status
## oct_type Primary Secondary Third level School/Colllege
## Favour 32 259 174 20
## Against 22 178 57 14
## Didn't Vote 0 0 0 0prop.table(table(oct_type,Education_status))## Education_status
## oct_type Primary Secondary Third level School/Colllege
## Favour 0.04232804 0.34259259 0.23015873 0.02645503
## Against 0.02910053 0.23544974 0.07539683 0.01851852
## Didn't Vote 0.00000000 0.00000000 0.00000000 0.00000000prop.table(table(oct_type,Education_status),1)## Education_status
## oct_type Primary Secondary Third level School/Colllege
## Favour 0.06597938 0.53402062 0.35876289 0.04123711
## Against 0.08118081 0.65682657 0.21033210 0.05166052
## Didn't Voteprop.table(table(oct_type,Education_status),2)## Education_status
## oct_type Primary Secondary Third level School/Colllege
## Favour 0.5925926 0.5926773 0.7532468 0.5882353
## Against 0.4074074 0.4073227 0.2467532 0.4117647
## Didn't Vote 0.0000000 0.0000000 0.0000000 0.0000000ggplot(Lisbon_data3,mapping = aes(oct_type,fill = Education_status))+geom_bar()+
geom_text(aes(label=..count..),stat="count",position=position_stack(0.5)) +
facet_grid(~Education_status)+
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("distribution of voting output based on Education status")
ggplot(Lisbon_data3,mapping = aes(oct_type,fill = Education_status))+geom_bar()+
geom_text(aes(label=round((..count../sum(..count..))*100,2)),
stat="count",position=position_stack(0.5)) + facet_grid(~Education_status)+
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("Percentage distribution of voting output based on Education status")
Q.6
ggplot(Lisbon_data2,mapping = aes(oct_vote,fill = EU_Knowledge))+geom_bar()+
geom_text(aes(label=..count..),stat="count",position=position_stack(0.5)) +
facet_grid(~EU_Knowledge)+
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("Distribution of individual who voted or not based EU knowledge")
ggplot(Lisbon_data2,mapping = aes(oct_type,fill = EU_Knowledge))+geom_bar()+
geom_text(aes(label=..count..),stat="count",position=position_stack(0.5)) +
facet_grid(~EU_Knowledge) +
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("distribution of voting output based on EU knowledge of individual")
ggplot(Lisbon_data2 , mapping = aes(oct_type,fill = EU_Knowledge))+geom_bar()+
geom_text(aes(label=round((..count../sum(..count..))*100,2)),
stat="count",position=position_stack(0.5)) + facet_grid(~EU_Knowledge)+
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("Percentage distribution of voting output based on EU knowledge of individual")
attach(Lisbon_data3)## The following objects are masked from Lisbon_data3 (pos = 3):
##
## Age, Age_type, Area_type, Class, Education_status, EU_Knowledge,
## Industry_type, June_type, June_vote, Marital, oct_type, oct_vote,
## Region, Registered_Voter, Sex, Switzerland_Member, Working_sector,
## Working_status## The following objects are masked from Lisbon_data2:
##
## Age, Age_type, Area_type, Class, Education_status, EU_Knowledge,
## Industry_type, June_type, June_vote, Marital, oct_type, oct_vote,
## Region, Registered_Voter, Sex, Switzerland_Member, Working_sector,
## Working_statustable(oct_type,EU_Knowledge)## EU_Knowledge
## oct_type Average Good Poor
## Favour 340 118 27
## Against 149 110 12
## Didn't Vote 0 0 0prop.table(table(oct_type,EU_Knowledge))## EU_Knowledge
## oct_type Average Good Poor
## Favour 0.44973545 0.15608466 0.03571429
## Against 0.19708995 0.14550265 0.01587302
## Didn't Vote 0.00000000 0.00000000 0.00000000prop.table(table(oct_type,EU_Knowledge),1)## EU_Knowledge
## oct_type Average Good Poor
## Favour 0.70103093 0.24329897 0.05567010
## Against 0.54981550 0.40590406 0.04428044
## Didn't Voteprop.table(table(oct_type,EU_Knowledge),2)## EU_Knowledge
## oct_type Average Good Poor
## Favour 0.6952965 0.5175439 0.6923077
## Against 0.3047035 0.4824561 0.3076923
## Didn't Vote 0.0000000 0.0000000 0.0000000ggplot(Lisbon_data3,mapping = aes(oct_type,fill = EU_Knowledge))+geom_bar()+
geom_text(aes(label=..count..),stat="count",position=position_stack(0.5)) +
facet_grid(~EU_Knowledge)+
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("distribution of voting output based on EU Knowledge of individual")
ggplot(Lisbon_data3,mapping = aes(oct_type,fill = EU_Knowledge))+geom_bar()+
geom_text(aes(label=round((..count../sum(..count..))*100,2)),
stat="count",position=position_stack(0.5)) + facet_grid(~EU_Knowledge)+
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("Percentage distribution of voting output based on EU Knowledge of individual")
Q.7
#To analyze is the Eu knowledge of individual have related to the questionare that switzerland is a member of Eu i will use chi square test.
table(EU_Knowledge,Switzerland_Member)## Switzerland_Member
## EU_Knowledge True False Don't Know
## Average 122 302 65
## Good 57 95 76
## Poor 14 19 6# H0 = Eu knowledge of individual is independent of switzerland is a member of Eu.
# H1 = Eu knowledge of individual is dependent of switzerland is a member of Eu.
chisq.test(table(EU_Knowledge,Switzerland_Member))##
## Pearson's Chi-squared test
##
## data: table(EU_Knowledge, Switzerland_Member)
## X-squared = 45.905, df = 4, p-value = 2.578e-09#p value is 2.2e-16 < 0.05 so i have to reject null hypothesis and accept alternate hypothesis.
#That means Eu knowledge of individual is dependent of switzerland is a member of Eu, and to analyze the dependancy i will use some plot's.
table(EU_Knowledge,Switzerland_Member)## Switzerland_Member
## EU_Knowledge True False Don't Know
## Average 122 302 65
## Good 57 95 76
## Poor 14 19 6prop.table(table(EU_Knowledge,Switzerland_Member))## Switzerland_Member
## EU_Knowledge True False Don't Know
## Average 0.161375661 0.399470899 0.085978836
## Good 0.075396825 0.125661376 0.100529101
## Poor 0.018518519 0.025132275 0.007936508prop.table(table(EU_Knowledge,Switzerland_Member),2)## Switzerland_Member
## EU_Knowledge True False Don't Know
## Average 0.63212435 0.72596154 0.44217687
## Good 0.29533679 0.22836538 0.51700680
## Poor 0.07253886 0.04567308 0.04081633prop.table(table(EU_Knowledge,Switzerland_Member),1)## Switzerland_Member
## EU_Knowledge True False Don't Know
## Average 0.2494888 0.6175869 0.1329243
## Good 0.2500000 0.4166667 0.3333333
## Poor 0.3589744 0.4871795 0.1538462ggplot(Lisbon_data2,mapping = aes(EU_Knowledge,fill = Switzerland_Member))+geom_bar()+
geom_text(aes(label=..count..),stat="count",position=position_stack(0.5)) +
facet_grid(~Switzerland_Member)+
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("Comparison of EU knowledge of an individual with the output of an questionare that is switzerland is a member of EU or not ")
ggplot(Lisbon_data2,mapping = aes(EU_Knowledge,fill = Switzerland_Member))+geom_bar()+
geom_text(aes(label=round((..count../sum(..count..))*100,2)),
stat="count",position=position_stack(0.5)) + facet_grid(~Switzerland_Member)+
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("Comparison in percentage of EU knowledge of an individual with the output of an questionare that is switzerland is a member of EU or not")
ggplot(Lisbon_data2,mapping = aes(oct_vote,fill = Switzerland_Member))+geom_bar()+
geom_text(aes(label=..count..),stat="count",position=position_stack(0.5)) +
facet_grid(~EU_Knowledge)+
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("Distribution of individual who voted or not based on both EU knowledge and Switzerland membership questionare output")
ggplot(Lisbon_data2,mapping = aes(oct_type,fill = EU_Knowledge)) + geom_bar() +
geom_text(aes(label=..count..),stat="count",position=position_stack(0.5)) +
facet_grid(~Switzerland_Member) +
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("distribution of voting output based on both EU knowledge and Switzerland membership questionare output")
ggplot(Lisbon_data2,mapping = aes(oct_type,fill = EU_Knowledge))+geom_bar()+
geom_text(aes(label=round((..count../sum(..count..))*100,2)),
stat="count",position=position_stack(0.5)) + facet_grid(~Switzerland_Member)+
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("distribution of voting output based on both EU knowledge and Switzerland membership questionare output")
table(Lisbon_data3$EU_Knowledge,Lisbon_data3$Switzerland_Member)##
## True False Don't Know
## Average 122 302 65
## Good 57 95 76
## Poor 14 19 6prop.table(table(Lisbon_data3$EU_Knowledge,Lisbon_data3$Switzerland_Member))##
## True False Don't Know
## Average 0.161375661 0.399470899 0.085978836
## Good 0.075396825 0.125661376 0.100529101
## Poor 0.018518519 0.025132275 0.007936508prop.table(table(Lisbon_data3$EU_Knowledge,Lisbon_data3$Switzerland_Member),2)##
## True False Don't Know
## Average 0.63212435 0.72596154 0.44217687
## Good 0.29533679 0.22836538 0.51700680
## Poor 0.07253886 0.04567308 0.04081633prop.table(table(Lisbon_data3$EU_Knowledge,Lisbon_data3$Switzerland_Member),1)##
## True False Don't Know
## Average 0.2494888 0.6175869 0.1329243
## Good 0.2500000 0.4166667 0.3333333
## Poor 0.3589744 0.4871795 0.1538462ggplot(Lisbon_data3,mapping = aes(oct_type,fill = EU_Knowledge))+geom_bar() +
geom_text(aes(label=..count..),stat="count",position=position_stack(0.5)) +
facet_grid(~Switzerland_Member) + theme(axis.text.x = element_text(angle = 90)) +
ggtitle("distribution of voting output based on both EU knowledge and Switzerland membership questionare output")
ggplot(Lisbon_data3,mapping = aes(oct_type,fill = EU_Knowledge)) + geom_bar() +
geom_text(aes(label=round((..count../sum(..count..))*100,2)),stat="count",
position=position_stack(0.5)) + facet_grid(~Switzerland_Member) +
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("distribution of voting output based on both EU knowledge and Switzerland membership questionare output")